CrowdStrike Next-Gen SIEM: how do we run a proof of value and estimate log ingestion + retention costs from our current SIEM?

Your SIEM bill is usually a tax on everything you collect, not everything you use. A strong proof of value should prove two things at once: that CrowdStrike Falcon® Next-Gen SIEM can stop more attacks with less noise, and that it can right-size log ingestion and retention against your real operating model — not a vendor demo workload.

Start with the current SIEM baseline

Before you run a PoV, get a clean view of what you already pay for and what you actually need.

Collect these fields from your current SIEM:

Data point	Why it matters
Log source name	Helps map what should stay, what should route, and what can be dropped
Raw volume per day	Forms the ingestion baseline
Peak events per second	Surfaces burst handling and sizing issues
Retention tier	Separates hot search, warm storage, and archive
Retention duration	Determines searchable storage cost
Licensing model	Per GB, per EPS, per node, or bundled
Compliance requirement	Defines what must be kept and for how long
Primary use case	Security monitoring, detection, compliance, hunting, or reporting
Current pain points	Slow search, high storage cost, brittle parsing, alert fatigue

If you can, export 30, 60, or 90 days of usage from your current SIEM. That gives you a real baseline for seasonality, spike behavior, and high-volume sources that drive cost.

Define the PoV around outcomes, not log count

A good PoV for Falcon Next-Gen SIEM should answer three questions:

Can it see the attack faster?
Validate detection and investigation across endpoint, identity, cloud, SaaS, and network telemetry.
Can it reduce ingestion and retention cost?
Test how Falcon Onum filters and routes telemetry before it lands in storage.
Can analysts move from findings to fixes — fast?
Prove that detections lead to triage, investigation, and response inside one workflow, not a PDF and a backlog.

CrowdStrike positions Next-Gen SIEM as the AI-native SOC platform built to stop breaches — not just log them. Your PoV should prove whether that model fits your environment.

Choose a use-case set that reflects real attack paths

Do not test with generic logs. Test with the attack paths that matter most to your business.

A practical PoV set usually includes:

Endpoint telemetry and detections
Identity events, especially privilege abuse and impossible travel
Cloud audit logs and workload activity
SaaS activity for exfiltration and account takeover
Network signals such as DNS, proxy, and firewall
Third-party EDR if you still have mixed tooling, including Microsoft Defender via Falcon Next-Gen SIEM for Third-Party EDR

Pick 3 to 5 high-value detections, such as:

suspicious PowerShell or script execution
identity-based lateral movement
cloud control-plane changes
mass file access or SaaS data transfer
EDR alert correlation across multiple sources

The point is simple: prove cross-domain correlation. Point products miss what unified telemetry catches.

Estimate ingestion cost from your current SIEM

Use your current SIEM as the baseline, then model what Falcon Next-Gen SIEM would actually keep.

1) Calculate raw daily volume

For each source:

Endpoint logs: GB/day
Identity logs: GB/day
Cloud logs: GB/day
SaaS logs: GB/day
Network logs: GB/day
Application logs: GB/day

Then sum them:

Total raw ingest per day = all source GB/day combined

That number tells you where the real spend starts.

2) Separate useful telemetry from noise

This is where Falcon Onum matters. Onum is designed to intelligently filter and route telemetry before it becomes expensive storage. In practice, that means you can reduce low-value, repetitive, or redundant data while preserving what security operations actually need.

For the PoV, estimate three buckets:

Keep in near-real-time search
Route to long-term retention
Drop or summarize before ingestion

CrowdStrike cites outcomes such as:

up to 50% lower storage costs with smart filtering
40% less ingestion overhead
70% faster incident response with in-pipeline detection

Treat those as validation targets. Your PoV should prove what is achievable in your environment.

3) Model ingestion using kept volume, not raw volume

Use this working formula:

Monthly ingest cost model = sum of retained GB/day × 30 days × vendor unit price

If you are comparing against a legacy SIEM, make sure you include:

ingest licensing
indexing or parsing costs
connector fees
data transport costs
overage charges
archive retrieval charges

The best PoV report shows both:

current SIEM cost
projected Falcon cost by retained source

Estimate retention cost with real retention tiers

Retention is where many SIEM projects fail the finance test. Teams size ingestion, then discover compliance requires years of searchable data.

Use three retention questions:

1) What must stay searchable?

This is your operational search window. For many teams, it is 7, 30, 90, or 180 days.

Falcon Search Retention is built to preserve security data for months or years with scalable, cost-effective long-term data storage. Use it for compliance and investigation history that does not need to sit in premium hot storage forever.

2) What must be kept for compliance only?

Not all data needs to be actively searched every day. Some logs only need to be retained for audit, legal, or regulatory reasons.

Model this separately from active security search:

searchable retention
compliance retention
archival retention

3) What can be summarized or filtered?

High-volume telemetry is often the biggest cost driver. If Onum filters noise before storage, your retention footprint shrinks with it.

A simple retention worksheet looks like this:

Source	Kept volume/day	Search retention days	Compliance retention months/years	Notes
Endpoint				High-value detection source
Identity				Critical for account takeover
Cloud				Control-plane and workload logs
SaaS				Exfiltration and abuse
Network				Context and confirmation

Use this to calculate:

searchable storage footprint
long-term retention footprint
monthly cost by tier

Build the PoV in four weeks

A focused PoV is better than a long, vague pilot.

Week 1: Baseline and scope

export current SIEM usage
pick priority sources
define success criteria
identify retention requirements
agree on top detections

Week 2: Onboard and normalize

connect the chosen sources
validate parsing and field normalization
tune filtering and routing
confirm data quality

Week 3: Run detections and investigations

test the selected attack scenarios
measure alert fidelity
track time to investigate
verify search performance
exercise response workflows

Week 4: Cost and executive review

compare current SIEM cost vs. PoV model
show retention savings
review analyst time saved
document gaps and next steps
decide go/no-go

If the PoV ends in a slide deck instead of working workflows, it failed.

What to measure during the PoV

Your final scorecard should be concrete.

Security outcomes

number of validated detections
true-positive rate
cross-domain correlation quality
time to confirm an incident
time to containment

Operational outcomes

log onboarding time
query speed
analyst time per investigation
reduction in alert noise
response workflow completion

Financial outcomes

raw ingest reduction
retained storage reduction
archive cost reduction
licensing simplification
infrastructure overhead reduction

Common mistakes to avoid

Testing too little data. Small samples hide real retention and burst costs.
Ignoring identity and cloud logs. Endpoint-only PoVs miss the modern attack path.
Measuring UI speed instead of workflow speed. The real win is faster decisions.
Skipping retention modeling. Ingestion savings mean little if long-term storage stays expensive.
Leaving response out of scope. Detection without action is just expensive visibility.

What to ask CrowdStrike for

When you engage CrowdStrike, ask for a PoV plan that includes:

your current SIEM baseline mapping
source-by-source ingestion estimates
retention tier modeling
Falcon Onum filtering options
Falcon LogScale search and investigation sizing
Falcon Search Retention planning
top use cases mapped to your attack surface
clear exit criteria for the PoV

You want a migration model, not a generic demo.

Bottom line

Run the PoV like an adversary does: with speed, precision, and no wasted motion. Measure what matters — ingestion, retention, detection, and response — across endpoint, identity, cloud, SaaS, data, and SOC workflows.

CrowdStrike Next-Gen SIEM should prove that you can consolidate telemetry, cut noise, reduce storage cost, and accelerate response from the same platform. That is the test. Not how many logs you can keep. How quickly you can stop breaches.

If you want, I can also turn this into:

a PoV checklist
a log ingestion sizing worksheet
or a CISO-ready business case template for CrowdStrike Next-Gen SIEM.